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Insights on the Relationship between Artificial Intelligence Skills and National Culture

Author

Listed:
  • Irina-Eugenia Iamandi

    (Bucharest University of Economic Studies, Romania)

  • Laura-Gabriela Constantin

    (Bucharest University of Economic Studies, Romania)

  • Sebastian Madalin Munteanu

    (Bucharest University of Economic Studies, Romania)

  • Bogdan Cernat-Gruici

    (Bucharest University of Economic Studies, Romania)

Abstract

In order to fully benefit from the advantages induced by artificial intelligence (AI) at the corporate level, business organisations should be well aware of the factors contributing to the proper AI specialisation of their employees. In this context, the present paper aims to examine the role of national culture in supporting the distribution of AI skills and jobs across countries, starting from the theoretical and applied need of carrying out such a study. The investigation is developed by using Hofstede s model of the six dimensions of national culture and it focusses on a global sample of 44 leading countries regarding their AI talent concentration. For the fulfilment of the assumed objective, multiple linear regression models, accompanied by robust regression and enhanced by beta regression with novel robust estimators, were employed. The achieved results reflect the existence of a connection between national culture and AI skills development across the explored countries. More specifically, the outcomes exhibit the prevalence of a negative, statistically significant, relationship between individualism and AI talent concentration, and a positive, significant connection between long-term orientation and AI skills and jobs concentration. The results are relevant for organisations and managers aiming to support the development of AI skills of their employees by nurturing free communication, group collaboration, teamwork, peer learning, and forward-looking strategic education programmes for adjustment to AI.

Suggested Citation

  • Irina-Eugenia Iamandi & Laura-Gabriela Constantin & Sebastian Madalin Munteanu & Bogdan Cernat-Gruici, 2024. "Insights on the Relationship between Artificial Intelligence Skills and National Culture," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(67), pages 741-741, August.
    • Handle: RePEc:aes:amfeco:v:26:y:2024:i:67:p:741
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    References listed on IDEAS

    as
    1. Cribari-Neto, Francisco & Zeileis, Achim, 2010. "Beta Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i02).
    2. Erumban, Abdul Azeez & de Jong, Simon B., 2006. "Cross-country differences in ICT adoption: A consequence of Culture?," Journal of World Business, Elsevier, vol. 41(4), pages 302-314, December.
    3. Grün, Bettina & Kosmidis, Ioannis & Zeileis, Achim, 2012. "Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i11).
    4. Vitolla, Filippo & Raimo, Nicola & Rubino, Michele & Garegnani, Giovanni Maria, 2021. "Do cultural differences impact ethical issues? Exploring the relationship between national culture and quality of code of ethics," Journal of International Management, Elsevier, vol. 27(1).
    5. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    artificial intelligence (AI); AI skills; national culture; Hofstede; organisational competitiveness; OLS multiple regression; robust regression models;
    All these keywords.

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • M50 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - General
    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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